Overall, our framework turns out the best approach, both when trained with and without ground-truth supervision. Indeed, even when trained in an unsupervised manner, it already outperforms supervised methods [1]}. Moreover, we can notice that the absolute difference between LiDAR values and proxy labels is already a good cue to remove outliers, easily outperforming Surface [2]} and often being better than supervised approaches [1]}. Our framework learns to leverage such proxy labels and steadily exceeds their limitations, leading to even better results. Focusing on the former Table REF  (a), we can notice how [2]} performs poorly at sparsification. Indeed, Surface performs a binary classification of inliers and outliers, removing only a small set of pixels (respectively 1.60% and 1.47% of the pixels with available ground-truth on CV split and 142 split), yet leaving many outliers on stage. Nonetheless, since a binary method is penalized by AUC evaluation, we will provide more fair comparisons in Sec. REF .
